Crypto perpetual swaps for assets like gold, oil and forex are trading around the clock and, according to recent data, forecast Wall Street’s Monday open with about 89% accuracy. That signal comes during the 68-hour gap from Friday’s close to Monday’s bell, when traders typically face “gap risk”—sudden price moves driven by geopolitical events or weekend news. Without reliable indicators, they either hold risky positions or pay for broad, expensive hedges. GapCast turns that weekend crypto action into a clear Monday forecast. It gathers volume and price data on tokenized perpetual swaps from platforms such as GMX and Synthetix, then runs correlations and sentiment analysis over Python/Pandas pipelines. The service outputs a directional score and forecast, delivered via dashboard, email or API. Early users can test the idea with a free “Weekend Correlation Calculator,” while deeper pockets—quants, hedge funds—can subscribe to higher-tier API or enterprise feeds. To attract users, GapCast will target specific asset searches (think programmatic SEO for “track crude oil perpetual swaps”) and team up with financial newsletters and trading influencers. The long game is building a proprietary dataset: the more weeks of data, the stronger the predictive model. That data moat, combined with niche focus on crypto-linked signals, differentiates GapCast from data giants like Bloomberg or Reuters. Under the hood, the MVP stack is straightforward. A FastAPI backend orchestrates cron-driven pulls of swap data, storing time-series feeds in TimescaleDB on PostgreSQL. Pandas handles the heavy correlation work and sentiment scoring. Frontend is a minimalist Next.js dashboard on Vercel, showing real-time scores and historical performance graphs. No bells, just the numbers traders need before the market reopens.